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Advancing Computational Intelligence to Enhance Health and Well-Being Provisions.

  • School: School of Science and Technology
  • Study mode(s): Full-time / Part-time
  • Starting: 2023
  • Funding: UK student / EU student (non-UK) / International student (non-EU) / Fully-funded


NTU's Fully-funded PhD Studentship Scheme 2023

Project ID: S&T16

The multi-sensor data collection capabilities of smart sensing devices are improving healthcare. They enable the generation of rich contextual data to monitor the behavioural patterns and other contextual information that unfold in everyday settings. Wearables and smartphones can wirelessly and non-invasively monitor vital signs (including heart rate, skin temperature, etc) as biomarkers that reflect associated human behavioural/activities. Routinely and continuously monitoring these signals is crucial for maintaining the health and well-being of individuals including vulnerable groups whose mental and physical conditions are volatile (such as autistic syndrome, depression, anxiety, and elderly). While Artificial Intelligence/Machine Learning (AI/ML) has been successful in tasks such as image processing, new methods will be needed to deal with user-generated digital biomarkers collected via sensing devices which by nature contain significant noises. This is important in the face of challenges presented in the determination and establishment of the link between physiological biomarkers and health/well-being which is a critical yet necessary objective. Today, the question is no longer about the need to adopt technology in health care settings but the choice of the technology that fit the appropriate context that provides desired outcomes. Consequently, the following challenges remain, extending existing multilevel and multivariate sensor fusion algorithms to provide a robust and complete data description of behavioural patterns; extending the state-ofart AI/ML time series-based algorithms (e.g. XGBoost, Deep Learning – LSTM, CNN etc.) and develop novel explainable AI/ML algorithms to meet the desired outcomes; new methodologies and computational intelligent platforms including web/mobile applications to provide health and mental well-being interventions. This PhD Research project will focus on the application of non-invasive sensing devices to predict and monitor the behavioural patterns/ health/wellness/mood specifically stress, depression, anxiety-related conditions of individuals including vulnerable groups. A range of sensing devices including wearables will be investigated to understand their efficacy and reliability in monitoring vital signs. It will also focus on extending existing multilevel, multivariate sensor fusion, algorithms, and AI/ML algorithms and how they can be integrated into intelligent healthcare Nottingham Trent University 2 systems to improve existing solutions to enhance the life experience of the targeted groups. Specifically, this research will examine and analyse data gathered from the sensor network in conjunction with the prevailing observation of the participating users’ behaviours whilst exploring the use of standard stress/anxiety/depression self-assessment tests. This will lead to the development of correlation maps that links the signal to the aforementioned behavioural patterns, setting the scene to inform intervention and care.

Supervisory Team:
Dr Isibor Kennedy Ihianle (DoS; T&R; ECR)
Dr Kayode Owa – (Co-Supervisor; ECR)
Dr Pedro Machado - (Co-Supervisor; ECR)
Prof Eiman Kanjo - (Co-Supervisor; Advanced Career with many PhD completions as DoS and Co-Supervisors, head of the Pervasive Computing Group)

Entry qualifications

For the eligibility criteria, visit our studentship application page.

How to apply

To make an application, please visit our studentship application page.

Fees and funding

This is part of NTU's 2023 fully-funded PhD Studentship Scheme.

Guidance and support

Application guidance can be found on our studentship application page.

Still need help?

+44 (0)115 941 8418